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PyTorch vs Torch: What are the differences?

PyTorch and Torch are both popular deep learning frameworks. Let's explore the key differences between them.

  1. Architecture and Development: The major difference between PyTorch and Torch lies in their architecture and development. PyTorch is based on Torch, but it has been re-engineered to provide a more dynamic and intuitive development experience. It includes features such as automatic differentiation, which allows developers to define and optimize computational graphs on the fly. In contrast, Torch uses a more static and declarative approach to building and optimizing computational graphs.

  2. Pythonic Interface: PyTorch is designed to have a more pythonic interface compared to Torch. It leverages the power and simplicity of Python, making it easier for developers to write and debug deep learning models. Torch, on the other hand, provides a Lua interface, which may require additional effort for developers who are not familiar with the language.

  3. Popularity and Community Support: PyTorch has gained significant popularity in recent years and has a large and active community. It has become the preferred choice for many researchers and practitioners in the deep learning community. Torch, while still widely used, may not have the same level of popularity and community support as PyTorch.

  4. Development and Maintenance: PyTorch is actively developed and maintained by Facebook's AI Research (FAIR) group. This ensures that the framework is constantly updated with new features and bug fixes. Torch, on the other hand, is primarily developed and maintained by a smaller group of developers. While it is still actively maintained, the development pace may not be as fast as PyTorch.

  5. Integration with Python Libraries: PyTorch seamlessly integrates with other popular Python libraries such as NumPy, SciPy, and scikit-learn. This allows developers to leverage the rich ecosystem of Python libraries and tools for data manipulation and analysis. Torch, being primarily Lua-based, may not have the same level of integration with Python libraries, although there are ways to bridge the two languages.

In summary, PyTorch offers a more dynamic and pythonic experience with a larger community, while Torch may be preferred by developers who are already familiar with Lua or have specific requirements.

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Pros of PyTorch
Pros of Torch
  • 15
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow
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    Cons of PyTorch
    Cons of Torch
    • 3
      Lots of code
    • 1
      It eats poop
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      What is PyTorch?

      PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

      What is Torch?

      It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

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      See which teams inside your own company are using PyTorch or Torch.
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